{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\onedr\Dropbox\Cook County Community Survey (Dana and Dave)\Student Projects\Descriptive Representation\RandP Submission\log_of_analysis.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}27 Jun 2024, 13:11:16

{com}. do "C:\Users\onedr\AppData\Local\Temp\STD41c_000000.tmp"
{txt}
{com}. use "replication_data.dta", clear
{txt}
{com}. 
. *** STANDARDIZE MEASURES
. foreach i in rate_commish trust_commish efficacy elect_interest intent_support {c -(}
{txt}  2{com}.         qui sum `i'
{txt}  3{com}.         gen std_`i'=(`i'-r(mean))/r(sd)
{txt}  4{com}. {c )-}
{txt}
{com}. label var std_rate_commish "Board Job Rating"
{txt}
{com}. label var std_trust "Trust Commissioner"
{txt}
{com}. label var std_efficacy "Efficacy"
{txt}
{com}. label var std_elect_interest "Interest in Election"
{txt}
{com}. label var std_intent_support "Intent to Support Commissioner"
{txt}
{com}. alpha std_*, i gen(summary_outcome)

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
std_rate_c~h{col 14}{c |}{res}{col 16}1199{col 24}+{col 31} 0.7716{col 45} 0.6230{col 59} .4516977{col 73} 0.7672
{txt}std_trust_~h{col 14}{c |}{res}{col 16}1199{col 24}+{col 31} 0.7632{col 45} 0.6106{col 59} .4569943{col 73} 0.7710
{txt}std_efficacy{col 14}{c |}{res}{col 16}1199{col 24}+{col 31} 0.8113{col 45} 0.6828{col 59} .4267129{col 73} 0.7486
{txt}std_elect_~t{col 14}{c |}{res}{col 16}1199{col 24}+{col 31} 0.6107{col 45} 0.4002{col 59} .5528808{col 73} 0.8318
{txt}std_intent~t{col 14}{c |}{res}{col 16}1199{col 24}+{col 31} 0.8174{col 45} 0.6922{col 59}  .422853{col 73} 0.7456
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4622277{col 73} 0.8112
{txt}{hline 13}{c BT}{hline 65}

{com}. qui sum summary_outcome
{txt}
{com}. replace summary_outcome=(summary_outcome-r(mean))/r(sd)
{txt}(1,199 real changes made)

{com}. label var summary_outcome "Favorably Inclined toward Board/Commissioner"
{txt}
{com}. 
. 
. ****SUMMARY STATS (TABLE SM.A2)
. estpost summarize rate_commish trust efficacy elect_interest intent_support age educ income income_mis Women Men Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other 

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:rate_commish}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.224354}}}{space 1}{space 1}{ralign 9:{res:{sf: .7234131}}}{space 1}{space 1}{ralign 9:{res:{sf: .8505369}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:trust_comm~h}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.535446}}}{space 1}{space 1}{ralign 9:{res:{sf: .6696524}}}{space 1}{space 1}{ralign 9:{res:{sf: .8183229}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:efficacy}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.034195}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.076459}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.037525}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:elect_inte~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.488741}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.351918}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.16272}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:intent_sup~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.773978}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.142945}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.069086}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 47.96163}}}{space 1}{space 1}{ralign 9:{res:{sf: 315.1905}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.75361}}}{space 1}{space 1}{ralign 9:{res:{sf:       18}}}{space 1}
{space 0}{space 0}{ralign 12:educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.005004}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.110159}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.452638}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:income}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.867749}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.52744}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.539413}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:income_mis}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0425354}}}{space 1}{space 1}{ralign 9:{res:{sf: .0407602}}}{space 1}{space 1}{ralign 9:{res:{sf: .2018915}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:Women}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .5387823}}}{space 1}{space 1}{ralign 9:{res:{sf: .2487034}}}{space 1}{space 1}{ralign 9:{res:{sf: .4987017}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:Men}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:  .442035}}}{space 1}{space 1}{ralign 9:{res:{sf: .2468459}}}{space 1}{space 1}{ralign 9:{res:{sf: .4968359}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Gend}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0191827}}}{space 1}{space 1}{ralign 9:{res:{sf: .0188304}}}{space 1}{space 1}{ralign 9:{res:{sf: .1372238}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .5537948}}}{space 1}{space 1}{ralign 9:{res:{sf: .2473124}}}{space 1}{space 1}{ralign 9:{res:{sf: .4973051}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_hisp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .1559633}}}{space 1}{space 1}{ralign 9:{res:{sf: .1317486}}}{space 1}{space 1}{ralign 9:{res:{sf: .3629719}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_black}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .2527106}}}{space 1}{space 1}{ralign 9:{res:{sf: .1890056}}}{space 1}{space 1}{ralign 9:{res:{sf: .4347477}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_aind}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0216847}}}{space 1}{space 1}{ralign 9:{res:{sf: .0212322}}}{space 1}{space 1}{ralign 9:{res:{sf: .1457128}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_hpi}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0083403}}}{space 1}{space 1}{ralign 9:{res:{sf: .0082776}}}{space 1}{space 1}{ralign 9:{res:{sf: .0909815}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_asian}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0533778}}}{space 1}{space 1}{ralign 9:{res:{sf: .0505708}}}{space 1}{space 1}{ralign 9:{res:{sf: .2248795}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:race_other}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf:     1199}}}{space 1}{space 1}{ralign 9:{res:{sf: .0183486}}}{space 1}{space 1}{ralign 9:{res:{sf:  .018027}}}{space 1}{space 1}{ralign 9:{res:{sf: .1342646}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:rate_commish}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:     3866}}}{space 1}
{space 0}{space 0}{ralign 12:trust_comm~h}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}{space 1}{ralign 9:{res:{sf:     3040}}}{space 1}
{space 0}{space 0}{ralign 12:efficacy}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:     3638}}}{space 1}
{space 0}{space 0}{ralign 12:elect_inte~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:     4183}}}{space 1}
{space 0}{space 0}{ralign 12:intent_sup~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf:     3326}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       88}}}{space 1}{space 1}{ralign 9:{res:{sf:    57506}}}{space 1}
{space 0}{space 0}{ralign 12:educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        6}}}{space 1}{space 1}{ralign 9:{res:{sf:     4802}}}{space 1}
{space 0}{space 0}{ralign 12:income}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       16}}}{space 1}{space 1}{ralign 9:{res:{sf: 8234.431}}}{space 1}
{space 0}{space 0}{ralign 12:income_mis}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       51}}}{space 1}
{space 0}{space 0}{ralign 12:Women}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      646}}}{space 1}
{space 0}{space 0}{ralign 12:Men}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      530}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Gend}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       23}}}{space 1}
{space 0}{space 0}{ralign 12:race_white}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      664}}}{space 1}
{space 0}{space 0}{ralign 12:race_hisp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      187}}}{space 1}
{space 0}{space 0}{ralign 12:race_black}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      303}}}{space 1}
{space 0}{space 0}{ralign 12:race_aind}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       26}}}{space 1}
{space 0}{space 0}{ralign 12:race_hpi}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       10}}}{space 1}
{space 0}{space 0}{ralign 12:race_asian}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       64}}}{space 1}
{space 0}{space 0}{ralign 12:race_other}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       22}}}{space 1}

{com}. eststo pooled, title("Full Sample")
{txt}
{com}. esttab pooled using "sumstats.tex", cells(mean(fmt(a2))) replace title(Summary Statistics \label{c -(}sumstats{c )-}) addnote(Cell entries are means.) nogaps booktabs wrap varwidth(40) b(%9.3f) se(%9.3f) star(+ 0.10 * 0.05 ** .01) nonotes nodepvars label collabel(none)
{res}{txt}{p 0 4 2}
(file {bf}
sumstats.tex{rm}
not found)
{p_end}
(output written to {browse  `"sumstats.tex"'})

{com}. 
. ****TABLE OF MEANS (TABLE SM.A4)
. xtable commis_name, c(mean std_rate_commish  mean std_trust mean std_efficacy mean std_elect_interest mean std_intent_support) file(means_names) replace

{txt}{hline 16}{c TT}{hline 69}
    commis_name {c |} mean(std_~h)  mean(std_~h)  mean(std_~y)  mean(std_~t)  mean(std_~t)
{hline 16}{c +}{hline 69}
     Alma Anaya {c |}     {res}.1205938       .132936      .0921544      .0386277      .1034875
     {txt}Bill Lowry {c |}    {res}-.2007934      .0003276     -.0759866     -.0978228       .002626
{txt}Brandon Johnson {c |}    {res}-.3908845      -.125884     -.0720327     -.0716726     -.0793099
 {txt}Bridget Degnen {c |}     {res}.0429328      .1160771      .0508531      .0377288      .0589085
 {txt}Bridget Gainer {c |}    {res}-.0533854      .0145691     -.1699239     -.2392788     -.1381203
    {txt}Dennis Deer {c |}     {res}.1370375      .0122303      .1203785      .0292303     -.1818683
   {txt}Donna Miller {c |}     {res}.1166038      .0405479      .0568103       .060275      .0784453
  {txt}Frank Aguilar {c |}    {res}-.1008066     -.1401087     -.0472727       .086322     -.0270594
     {txt}John Daley {c |}     {res}-.182131     -.2639566     -.1668239      .0216288     -.1393511
 {txt}Kevin Morrison {c |}     {res}-.055422      .0262926       .009743     -.0284199     -.0727498
  {txt}Scott Britton {c |}     {res}.0993136      .0465381      .0662596      .0223315     -.0361844
  {txt}Stanley Moore {c |}     {res}.0721435     -.0433157     -.0329584     -.2974778      .1111967
{txt}{hline 16}{c BT}{hline 69}
{res}Output written to {browse  means_names.xlsx}
{txt}
{com}. xtable commis_name, c(mean familiar_commish mean correct_inference) file(means_names_famil) replace

{txt}{hline 16}{c TT}{hline 41}
    commis_name {c |}      mean(famili~h)       mean(correc~e)
{hline 16}{c +}{hline 41}
     Alma Anaya {c |} {res}.294685989618301392             .7115384
     {txt}Bill Lowry {c |} {res}.259259253740310669             .8214286
{txt}Brandon Johnson {c |} {res}.324324309825897217             .9189189
 {txt}Bridget Degnen {c |} {res}.184782609343528748             .8695652
 {txt}Bridget Gainer {c |} {res}.315789461135864258             .8631579
    {txt}Dennis Deer {c |} {res}.227272734045982361             .9090909
   {txt}Donna Miller {c |} {res}.339901477098464966             .8627451
  {txt}Frank Aguilar {c |} {res}.356783926486968994             .8514851
     {txt}John Daley {c |} {res}.647887349128723145             .8472222
 {txt}Kevin Morrison {c |} {res}.192307695746421814             .8987342
  {txt}Scott Britton {c |} {res}.358208954334259033             .8382353
  {txt}Stanley Moore {c |} {res}.268292695283889771             .7619048
{txt}{hline 16}{c BT}{hline 41}
{res}Output written to {browse  means_names_famil.xlsx}
{txt}
{com}. xtable commis_race, c(mean std_rate_commish  mean std_trust mean std_efficacy mean std_elect_interest mean std_intent_support) file(means_race) replace

{txt}{hline 10}{c TT}{hline 69}
commis_ra {c |}
ce        {c |} mean(std_~h)  mean(std_~h)  mean(std_~y)  mean(std_~t)  mean(std_~t)
{hline 10}{c +}{hline 69}
        0 {c |}    {res}-.0292124     -.0041872     -.0448282     -.0453936     -.0638988
        {txt}1 {c |}     {res}.0115136     -.0015884       .023461      .0621259      .0391693
        {txt}2 {c |}     {res}.0186415      .0061391      .0224053     -.0183859      .0258055
{txt}{hline 10}{c BT}{hline 69}
Output written to {browse  means_race.xlsx}

{com}. xtable commis_race, c(mean familiar_commish mean correct_inference) file(means_race_famil) replace

{txt}{hline 10}{c TT}{hline 41}
commis_ra {c |}
ce        {c |}      mean(famili~h)       mean(correc~e)
{hline 10}{c +}{hline 41}
        0 {c |} {res}.327543437480926514              .864532
        {txt}1 {c |}  {res}.32512316107749939             .7804878
        {txt}2 {c |} {res}.306068599224090576             .8563969
{txt}{hline 10}{c BT}{hline 41}
Output written to {browse  means_race_famil.xlsx}

{com}. xtable commis_female, c(mean std_rate_commish  mean std_trust mean std_efficacy mean std_elect_interest mean std_intent_support) file(means_gender) replace

{txt}{hline 10}{c TT}{hline 69}
commis_fe {c |}
male      {c |} mean(std_~h)  mean(std_~h)  mean(std_~y)  mean(std_~t)  mean(std_~t)
{hline 10}{c +}{hline 69}
        0 {c |}    {res}-.0795814      -.079976     -.0321552     -.0017837     -.0497107
        {txt}1 {c |}     {res}.0797143      .0801095      .0322089      .0017867      .0497937
{txt}{hline 10}{c BT}{hline 69}
Output written to {browse  means_gender.xlsx}

{com}. xtable commis_female, c(mean familiar_commish mean correct_inference) file(means_gender_famil) replace

{txt}{hline 10}{c TT}{hline 41}
commis_fe {c |}
male      {c |}      mean(famili~h)       mean(correc~e)
{hline 10}{c +}{hline 41}
        0 {c |} {res}.343485623598098755                 .855
        {txt}1 {c |}  {res}.29648241400718689             .8113523
{txt}{hline 10}{c BT}{hline 41}
Output written to {browse  means_gender_famil.xlsx}

{com}. 
. 
. ****MAIN REGRESSIONS (TABLE SM.A5; Estimates reported in FIGURE 1)
. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis Women Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other  i.names_index, r      
{txt}  3{com}. estimates store est_`i'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. coefplot est_std_rate_commish est_std_trust est_std_efficacy est_std_elect_interest est_std_intent_support , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.125).5, labsize(small)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Estimated Treatment Effect (in Standard Deviations)", size(medlarge))  text(.66 -.15 "Board Job Rating" .825 -.23 "Trust Commissioner" .99 -.09 "Efficacy" 1.16 -.36 "Interest in Election" 1.33 -.16 "Intent to Support" 1.66 .26 "Board Job Rating" 1.825 .26 "Trust Commissioner" 1.99 .14 "Efficacy" 2.16 .21 "Interest in Election" 2.33 .19 "Intent to Support",  size(small))
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph save full_sample, replace
{txt}{p 0 4 2}
(file {bf}
full_sample.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:full_sample.gph} saved

{com}. graph export "direct_fx.eps", as(eps) replace
{txt}{p 0 4 2}
(file {bf}
direct_fx.eps{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
direct_fx.eps{rm}
saved as
EPS
format
{p_end}

{com}. graph export "direct_fx.tif", as(tif) replace
{txt}{p 0 4 2}
(file {bf}
direct_fx.tif{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
direct_fx.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. esttab est_std_rate_commish est_std_trust est_std_efficacy est_std_elect_interest est_std_intent_support using base_regressions.tex, replace nogaps compress booktabs wrap keep(comm_racematch comm_gendermatch age educ income income_mis Women Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other) varwidth(30) b(%9.3f) se(%9.3f) star(* 0.05 ** .01) nonotes label title("Estimated Effects of Descriptive Rep. Signals"\label{c -(}baseregs{c )-} \scriptsize) addnote("Cell entries are unstandardized OLS coefficients; robust standard errors in parentheses; $* p < .05; ** p < .01$." "Outcome variables: mean = 0, standard deviation = 1.  All models control for a vector of indicators for the 11 of the 12 Commissioners used as treatments.")
{res}{txt}{p 0 4 2}
(file {bf}
base_regressions.tex{rm}
not found)
{p_end}
(output written to {browse  `"base_regressions.tex"'})

{com}. 
. 
. **WITHOUT KNOWN (TABLE SM.A8)
. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis Women Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other  i.names_index if commis_name!="Brandon Johnson" & commis_name!="John Daley", r        
{txt}  3{com}. estimates store est_`i'_NK
{txt}  4{com}. {c )-}
{txt}
{com}. coefplot est_std_rate_commish_NK est_std_trust_NK est_std_efficacy_NK est_std_elect_interest_NK est_std_intent_support_NK , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.125).5, labsize(small)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Estimated Treatment Effect (in Standard Deviations)", size(medlarge))  text(.66 -.17 "Board Job Rating" .825 -.21 "Trust Commissioner" .99 -.11 "Efficacy" 1.16 -.38 "Interest in Election" 1.33 -.16 "Intent to Support" 1.66 .26 "Board Job Rating" 1.825 .26 "Trust Commissioner" 1.99 .11 "Efficacy" 2.16 .21 "Interest in Election" 2.33 .19 "Intent to Support",  size(small))
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph save full_sample, replace
{res}{txt}file {bf:full_sample.gph} saved

{com}. graph export "direct_fx_not_famous.eps", as(eps) replace
{txt}{p 0 4 2}
(file {bf}
direct_fx_not_famous.eps{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
direct_fx_not_famous.eps{rm}
saved as
EPS
format
{p_end}

{com}. graph export "direct_fx_not_famous.tif", as(tif) replace
{txt}{p 0 4 2}
(file {bf}
direct_fx_not_famous.tif{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
direct_fx_not_famous.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. esttab est_std_rate_commish_NK est_std_trust_NK est_std_efficacy_NK est_std_elect_interest_NK est_std_intent_support_NK using base_regressions_notknown.tex, replace nogaps compress booktabs wrap keep(comm_racematch comm_gendermatch age educ income income_mis Women Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other) varwidth(30) b(%9.3f) se(%9.3f) star(* 0.05 ** .01) nonotes label title("Estimated Effects of Descriptive Rep. Signals (Exclude Most Recognized Commissioners)"\label{c -(}baseregs_notknown{c )-} \scriptsize) addnote("Cell entries are unstandardized OLS coefficients; robust standard errors in parentheses; $* p < .05; ** p < .01$." "Outcome variables: mean = 0, standard deviation = 1.  All models control for a vector of indicators for the 11 of the 12 Commissioners used as treatments. Models excludes cases where respondents were treated with Commissioner Brandon Johnson or John Daley.")
{res}{txt}{p 0 4 2}
(file {bf}
base_regressions_notknown.tex{rm}
not found)
{p_end}
(output written to {browse  `"base_regressions_notknown.tex"'})

{com}. 
. 
. **** BY R'S GROUP (FIGURE 2)
. label var comm_racematch "Race"
{txt}
{com}. label var comm_gendermatch "Gender"
{txt}
{com}. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis if (race_black|race_hisp|race_white) & gender!=3, r      
{txt}  3{com}. estimates store est_`i'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. coefplot est_std_rate_commish est_std_trust est_std_efficacy est_std_elect_interest est_std_intent_support , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.5).5) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Treatment Effect") title("All") text(.66 -.15 "Rating" .825 -.20 "Trust" .99 -.20 "Efficacy" 1.16 -.39 "Interest" 1.33 -.23 "Support", size(small))
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph save full_sample_nolab, replace
{txt}{p 0 4 2}
(file {bf}
full_sample_nolab.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:full_sample_nolab.gph} saved

{com}. 
. foreach g in Women Men White Black Hispanic{c -(}
{txt}  2{com}.         qui count if `g'
{txt}  3{com}.         local n=r(N)
{txt}  4{com}. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  5{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis if `g'==1 & (race_black|race_hisp|race_white) & gender!=3, r     
{txt}  6{com}. estimates store est_`i'
{txt}  7{com}. {c )-}
{txt}  8{com}. 
. coefplot est_std_rate_commish est_std_trust est_std_efficacy est_std_elect_interest est_std_intent_support , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label plotlabels("Board Job Rating" "Trust Commissioner" "Efficacy" "Interest" "Intent to Support" ) legend(off) xtitle("Treatment Effect") title("`g' (N = `n')")
{txt}  9{com}. graph save `g', replace
{txt} 10{com}. {c )-}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Women.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Women.gph} saved
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Men.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Men.gph} saved
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
White.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:White.gph} saved
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Black.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Black.gph} saved
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
Hispanic.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:Hispanic.gph} saved

{com}. 
. graph combine full_sample_nolab.gph Women.gph Men.gph White.gph Black.gph Hispanic.gph,xcommon scheme(s1mono)
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph export "fx_by_group.eps", as(eps) replace
{txt}{p 0 4 2}
(file {bf}
fx_by_group.eps{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
fx_by_group.eps{rm}
saved as
EPS
format
{p_end}

{com}. graph export "fx_by_group.tif", as(tif) replace
{txt}{p 0 4 2}
(file {bf}
fx_by_group.tif{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
fx_by_group.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. 
. *SUMMARY OUTCOME MEASURE (TABLE SM.A6)
. qui reg summary_outcome comm_racematch comm_gendermatch age educ income income_mis if (race_black|race_hisp|race_white) & gender!=3, r  
{txt}
{com}. estimates store summary_pooled
{txt}
{com}. foreach g in Women Men White Black Hispanic{c -(}
{txt}  2{com}. reg summary_outcome comm_racematch comm_gendermatch age educ income income_mis if `g'==1 & (race_black|race_hisp|race_white) & gender!=3, r     
{txt}  3{com}. estimates store summary_`g'
{txt}  4{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}       601
                                                {txt}F(6, 594)         =  {res}     2.44
                                                {txt}Prob > F          = {res}    0.0245
                                                {txt}R-squared         = {res}    0.0194
                                                {txt}Root MSE          =    {res} .89402

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} summary_outcome{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2} .1215548{col 30}{space 2}  .075185{col 41}{space 1}    1.62{col 50}{space 3}0.106{col 58}{space 4}-.0261059{col 71}{space 3} .2692156
{txt}comm_gendermatch {c |}{col 18}{res}{space 2} .1434661{col 30}{space 2}  .072935{col 41}{space 1}    1.97{col 50}{space 3}0.050{col 58}{space 4} .0002242{col 71}{space 3}  .286708
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0019013{col 30}{space 2} .0020109{col 41}{space 1}   -0.95{col 50}{space 3}0.345{col 58}{space 4}-.0058506{col 71}{space 3} .0020481
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0151995{col 30}{space 2} .0307577{col 41}{space 1}    0.49{col 50}{space 3}0.621{col 58}{space 4}-.0452074{col 71}{space 3} .0756065
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0142071{col 30}{space 2}  .013842{col 41}{space 1}   -1.03{col 50}{space 3}0.305{col 58}{space 4}-.0413924{col 71}{space 3} .0129782
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.2784243{col 30}{space 2}  .142484{col 41}{space 1}   -1.95{col 50}{space 3}0.051{col 58}{space 4} -.558258{col 71}{space 3} .0014094
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.0823829{col 30}{space 2} .1371847{col 41}{space 1}   -0.60{col 50}{space 3}0.548{col 58}{space 4}-.3518088{col 71}{space 3} .1870431
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}       495
                                                {txt}F(6, 488)         =  {res}     6.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0658
                                                {txt}Root MSE          =    {res} 1.0877

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} summary_outcome{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.1015176{col 30}{space 2} .1049796{col 41}{space 1}   -0.97{col 50}{space 3}0.334{col 58}{space 4}-.3077855{col 71}{space 3} .1047502
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.1020504{col 30}{space 2} .0983215{col 41}{space 1}   -1.04{col 50}{space 3}0.300{col 58}{space 4}-.2952363{col 71}{space 3} .0911354
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0111146{col 30}{space 2} .0028058{col 41}{space 1}   -3.96{col 50}{space 3}0.000{col 58}{space 4}-.0166275{col 71}{space 3}-.0056017
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1141309{col 30}{space 2} .0382787{col 41}{space 1}    2.98{col 50}{space 3}0.003{col 58}{space 4} .0389194{col 71}{space 3} .1893424
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0055008{col 30}{space 2} .0161124{col 41}{space 1}   -0.34{col 50}{space 3}0.733{col 58}{space 4}-.0371591{col 71}{space 3} .0261574
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.7540616{col 30}{space 2}  .199808{col 41}{space 1}   -3.77{col 50}{space 3}0.000{col 58}{space 4}-1.146652{col 71}{space 3}-.3614715
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .3728863{col 30}{space 2}  .190351{col 41}{space 1}    1.96{col 50}{space 3}0.051{col 58}{space 4}-.0011224{col 71}{space 3} .7468951
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}       656
                                                {txt}F(6, 649)         =  {res}     6.18
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0512
                                                {txt}Root MSE          =    {res} 1.0215

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} summary_outcome{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.0616381{col 30}{space 2} .0842473{col 41}{space 1}   -0.73{col 50}{space 3}0.465{col 58}{space 4}-.2270683{col 71}{space 3} .1037922
{txt}comm_gendermatch {c |}{col 18}{res}{space 2} .0041615{col 30}{space 2} .0798022{col 41}{space 1}    0.05{col 50}{space 3}0.958{col 58}{space 4}-.1525402{col 71}{space 3} .1608632
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0074976{col 30}{space 2} .0022634{col 41}{space 1}   -3.31{col 50}{space 3}0.001{col 58}{space 4}-.0119421{col 71}{space 3}-.0030531
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0805075{col 30}{space 2}  .032038{col 41}{space 1}    2.51{col 50}{space 3}0.012{col 58}{space 4} .0175967{col 71}{space 3} .1434182
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0091468{col 30}{space 2} .0141891{col 41}{space 1}    0.64{col 50}{space 3}0.519{col 58}{space 4}-.0187154{col 71}{space 3} .0370089
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.5215714{col 30}{space 2} .1270904{col 41}{space 1}   -4.10{col 50}{space 3}0.000{col 58}{space 4}-.7711295{col 71}{space 3}-.2720134
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .0186005{col 30}{space 2} .1918888{col 41}{space 1}    0.10{col 50}{space 3}0.923{col 58}{space 4}-.3581974{col 71}{space 3} .3953984
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}       295
                                                {txt}F(6, 288)         =  {res}     0.95
                                                {txt}Prob > F          = {res}    0.4567
                                                {txt}R-squared         = {res}    0.0225
                                                {txt}Root MSE          =    {res} .99161

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} summary_outcome{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2} .1590153{col 30}{space 2}  .122899{col 41}{space 1}    1.29{col 50}{space 3}0.197{col 58}{space 4}-.0828789{col 71}{space 3} .4009095
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.1046139{col 30}{space 2} .1177265{col 41}{space 1}   -0.89{col 50}{space 3}0.375{col 58}{space 4}-.3363274{col 71}{space 3} .1270996
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0008078{col 30}{space 2} .0035701{col 41}{space 1}    0.23{col 50}{space 3}0.821{col 58}{space 4}-.0062191{col 71}{space 3} .0078346
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0011816{col 30}{space 2}  .048042{col 41}{space 1}    0.02{col 50}{space 3}0.980{col 58}{space 4}-.0933763{col 71}{space 3} .0957396
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0399149{col 30}{space 2} .0211876{col 41}{space 1}   -1.88{col 50}{space 3}0.061{col 58}{space 4}-.0816172{col 71}{space 3} .0017873
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2} .1505642{col 30}{space 2} .5102371{col 41}{space 1}    0.30{col 50}{space 3}0.768{col 58}{space 4}-.8537024{col 71}{space 3} 1.154831
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2372033{col 30}{space 2} .2151431{col 41}{space 1}    1.10{col 50}{space 3}0.271{col 58}{space 4}-.1862489{col 71}{space 3} .6606554
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}       185
                                                {txt}F(6, 178)         =  {res}     1.16
                                                {txt}Prob > F          = {res}    0.3322
                                                {txt}R-squared         = {res}    0.0244
                                                {txt}Root MSE          =    {res} .96978

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} summary_outcome{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.1333344{col 30}{space 2} .1441564{col 41}{space 1}   -0.92{col 50}{space 3}0.356{col 58}{space 4}-.4178099{col 71}{space 3} .1511412
{txt}comm_gendermatch {c |}{col 18}{res}{space 2} .1761278{col 30}{space 2} .1478979{col 41}{space 1}    1.19{col 50}{space 3}0.235{col 58}{space 4}-.1157312{col 71}{space 3} .4679867
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0053517{col 30}{space 2} .0057153{col 41}{space 1}   -0.94{col 50}{space 3}0.350{col 58}{space 4}-.0166301{col 71}{space 3} .0059268
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0450891{col 30}{space 2}  .061502{col 41}{space 1}    0.73{col 50}{space 3}0.464{col 58}{space 4}-.0762778{col 71}{space 3} .1664561
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0305476{col 30}{space 2} .0244953{col 41}{space 1}   -1.25{col 50}{space 3}0.214{col 58}{space 4}-.0788862{col 71}{space 3} .0177909
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.4072996{col 30}{space 2} .2069664{col 41}{space 1}   -1.97{col 50}{space 3}0.051{col 58}{space 4}-.8157232{col 71}{space 3}  .001124
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .1836781{col 30}{space 2} .2601602{col 41}{space 1}    0.71{col 50}{space 3}0.481{col 58}{space 4}-.3297171{col 71}{space 3} .6970734
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. esttab summary_pooled summary_Women summary_Men summary_White summary_Black summary_Hispanic using summary_outcome.tex, replace nogaps compress booktabs wrap  varwidth(30) b(%9.3f) se(%9.3f) star(* 0.05 ** .01) nonotes label title("Mean Index as Outcome Variable"\label{c -(}summaryoutcome{c )-} \scriptsize)  addnote("Cell entries are unstandardized OLS coefficients; robust standard errors in parentheses; $* p < .05; ** p < .01$." "Outcome variable: mean = 0, standard deviation = 1. Restricted to respondents who identified as Men or Women and White, Black, or Hispanic.")
{res}{txt}{p 0 4 2}
(file {bf}
summary_outcome.tex{rm}
not found)
{p_end}
(output written to {browse  `"summary_outcome.tex"'})

{com}. 
. 
. ***INTERACTIONS (TABLE SM.A7)
. foreach v in Black Hispanic Women{c -(}
{txt}  2{com}.         gen `v'Xrace=`v'*comm_racematch 
{txt}  3{com}.         gen `v'Xgender=`v'*comm_gendermatch 
{txt}  4{com}. label var `v'Xrace "`v' x Race Match"
{txt}  5{com}. label var `v'Xgender "`v' x Gender Match"
{txt}  6{com}.         {c )-}
{txt}
{com}. 
. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. reg `i' comm_racematch comm_gendermatch  Black Hispanic Women  BlackXrace BlackXgender HispanicXrace HispanicXgender WomenXrace WomenXgender age educ income income_mis if (race_black|race_hisp|race_white) & gender!=3, r
{txt}  3{com}. test BlackXrace ==HispanicXrace
{txt}  4{com}. test BlackXgender==HispanicXgender
{txt}  5{com}. estimates store X_est_`i'
{txt}  6{com}. {c )-}

{txt}Linear regression                               Number of obs     = {res}     1,096
                                                {txt}F(15, 1080)       =  {res}     2.93
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0369
                                                {txt}Root MSE          =    {res} .99355

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}std_rate_commish{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.0479099{col 30}{space 2} .1201793{col 41}{space 1}   -0.40{col 50}{space 3}0.690{col 58}{space 4}-.2837213{col 71}{space 3} .1879015
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.1246534{col 30}{space 2} .1126672{col 41}{space 1}   -1.11{col 50}{space 3}0.269{col 58}{space 4}-.3457247{col 71}{space 3}  .096418
{txt}{space 11}Black {c |}{col 18}{res}{space 2}-.1286544{col 30}{space 2} .1174127{col 41}{space 1}   -1.10{col 50}{space 3}0.273{col 58}{space 4}-.3590372{col 71}{space 3} .1017285
{txt}{space 8}Hispanic {c |}{col 18}{res}{space 2}-.1236819{col 30}{space 2} .1356068{col 41}{space 1}   -0.91{col 50}{space 3}0.362{col 58}{space 4}-.3897646{col 71}{space 3} .1424008
{txt}{space 11}Women {c |}{col 18}{res}{space 2}-.4114468{col 30}{space 2} .0969642{col 41}{space 1}   -4.24{col 50}{space 3}0.000{col 58}{space 4}-.6017064{col 71}{space 3}-.2211873
{txt}{space 6}BlackXrace {c |}{col 18}{res}{space 2} .1851451{col 30}{space 2} .1410569{col 41}{space 1}    1.31{col 50}{space 3}0.190{col 58}{space 4}-.0916316{col 71}{space 3} .4619218
{txt}{space 4}BlackXgender {c |}{col 18}{res}{space 2}  .103231{col 30}{space 2} .1366264{col 41}{space 1}    0.76{col 50}{space 3}0.450{col 58}{space 4}-.1648523{col 71}{space 3} .3713143
{txt}{space 3}HispanicXrace {c |}{col 18}{res}{space 2}-.1409501{col 30}{space 2} .1720141{col 41}{space 1}   -0.82{col 50}{space 3}0.413{col 58}{space 4}-.4784697{col 71}{space 3} .1965695
{txt}{space 1}HispanicXgender {c |}{col 18}{res}{space 2} .0999809{col 30}{space 2} .1667616{col 41}{space 1}    0.60{col 50}{space 3}0.549{col 58}{space 4}-.2272326{col 71}{space 3} .4271944
{txt}{space 6}WomenXrace {c |}{col 18}{res}{space 2} .1753403{col 30}{space 2} .1280393{col 41}{space 1}    1.37{col 50}{space 3}0.171{col 58}{space 4}-.0758936{col 71}{space 3} .4265742
{txt}{space 4}WomenXgender {c |}{col 18}{res}{space 2} .3015288{col 30}{space 2} .1221954{col 41}{space 1}    2.47{col 50}{space 3}0.014{col 58}{space 4} .0617615{col 71}{space 3}  .541296
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0030168{col 30}{space 2}  .001908{col 41}{space 1}   -1.58{col 50}{space 3}0.114{col 58}{space 4}-.0067607{col 71}{space 3} .0007271
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0266786{col 30}{space 2} .0245065{col 41}{space 1}    1.09{col 50}{space 3}0.277{col 58}{space 4}-.0214073{col 71}{space 3} .0747644
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0008445{col 30}{space 2} .0103438{col 41}{space 1}   -0.08{col 50}{space 3}0.935{col 58}{space 4}-.0211406{col 71}{space 3} .0194517
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.4204673{col 30}{space 2}  .127622{col 41}{space 1}   -3.29{col 50}{space 3}0.001{col 58}{space 4}-.6708825{col 71}{space 3} -.170052
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2796687{col 30}{space 2} .1748854{col 41}{space 1}    1.60{col 50}{space 3}0.110{col 58}{space 4}-.0634849{col 71}{space 3} .6228223
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} BlackXrace - HispanicXrace = 0{p_end}

{txt}       F(  1,  1080) ={res}    2.76
{txt}{col 13}Prob > F ={res}    0.0970

{p 0 7}{space 1}{text:( 1)}{space 1} BlackXgender - HispanicXgender = 0{p_end}

{txt}       F(  1,  1080) ={res}    0.00
{txt}{col 13}Prob > F ={res}    0.9863

{txt}Linear regression                               Number of obs     = {res}     1,096
                                                {txt}F(15, 1080)       =  {res}     1.97
                                                {txt}Prob > F          = {res}    0.0149
                                                {txt}R-squared         = {res}    0.0279
                                                {txt}Root MSE          =    {res} .99804

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}std_trust_comm~h{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}   .02638{col 30}{space 2} .1160692{col 41}{space 1}    0.23{col 50}{space 3}0.820{col 58}{space 4}-.2013667{col 71}{space 3} .2541267
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.1599671{col 30}{space 2} .1086726{col 41}{space 1}   -1.47{col 50}{space 3}0.141{col 58}{space 4}-.3732005{col 71}{space 3} .0532662
{txt}{space 11}Black {c |}{col 18}{res}{space 2}-.1385615{col 30}{space 2} .1230589{col 41}{space 1}   -1.13{col 50}{space 3}0.260{col 58}{space 4}-.3800232{col 71}{space 3} .1029001
{txt}{space 8}Hispanic {c |}{col 18}{res}{space 2}-.1612171{col 30}{space 2}  .135196{col 41}{space 1}   -1.19{col 50}{space 3}0.233{col 58}{space 4}-.4264937{col 71}{space 3} .1040596
{txt}{space 11}Women {c |}{col 18}{res}{space 2}-.2815918{col 30}{space 2} .0980712{col 41}{space 1}   -2.87{col 50}{space 3}0.004{col 58}{space 4}-.4740235{col 71}{space 3}-.0891601
{txt}{space 6}BlackXrace {c |}{col 18}{res}{space 2} .0593947{col 30}{space 2} .1501993{col 41}{space 1}    0.40{col 50}{space 3}0.693{col 58}{space 4}-.2353207{col 71}{space 3} .3541102
{txt}{space 4}BlackXgender {c |}{col 18}{res}{space 2}  .052818{col 30}{space 2} .1439582{col 41}{space 1}    0.37{col 50}{space 3}0.714{col 58}{space 4}-.2296514{col 71}{space 3} .3352873
{txt}{space 3}HispanicXrace {c |}{col 18}{res}{space 2}-.3202548{col 30}{space 2} .1647232{col 41}{space 1}   -1.94{col 50}{space 3}0.052{col 58}{space 4}-.6434686{col 71}{space 3} .0029591
{txt}{space 1}HispanicXgender {c |}{col 18}{res}{space 2} .1990603{col 30}{space 2} .1593173{col 41}{space 1}    1.25{col 50}{space 3}0.212{col 58}{space 4}-.1135461{col 71}{space 3} .5116668
{txt}{space 6}WomenXrace {c |}{col 18}{res}{space 2} .0826845{col 30}{space 2} .1298814{col 41}{space 1}    0.64{col 50}{space 3}0.525{col 58}{space 4} -.172164{col 71}{space 3}  .337533
{txt}{space 4}WomenXgender {c |}{col 18}{res}{space 2} .3033598{col 30}{space 2} .1230746{col 41}{space 1}    2.46{col 50}{space 3}0.014{col 58}{space 4} .0618674{col 71}{space 3} .5448522
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0030606{col 30}{space 2} .0018724{col 41}{space 1}   -1.63{col 50}{space 3}0.102{col 58}{space 4}-.0067346{col 71}{space 3} .0006134
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0531437{col 30}{space 2} .0253315{col 41}{space 1}    2.10{col 50}{space 3}0.036{col 58}{space 4} .0034393{col 71}{space 3} .1028482
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0132531{col 30}{space 2} .0111694{col 41}{space 1}   -1.19{col 50}{space 3}0.236{col 58}{space 4}-.0351693{col 71}{space 3} .0086632
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.2979481{col 30}{space 2}  .155018{col 41}{space 1}   -1.92{col 50}{space 3}0.055{col 58}{space 4}-.6021186{col 71}{space 3} .0062224
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2153813{col 30}{space 2} .1725902{col 41}{space 1}    1.25{col 50}{space 3}0.212{col 58}{space 4}-.1232689{col 71}{space 3} .5540314
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} BlackXrace - HispanicXrace = 0{p_end}

{txt}       F(  1,  1080) ={res}    3.53
{txt}{col 13}Prob > F ={res}    0.0605

{p 0 7}{space 1}{text:( 1)}{space 1} BlackXgender - HispanicXgender = 0{p_end}

{txt}       F(  1,  1080) ={res}    0.58
{txt}{col 13}Prob > F ={res}    0.4460

{txt}Linear regression                               Number of obs     = {res}     1,096
                                                {txt}F(15, 1080)       =  {res}     3.29
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0386
                                                {txt}Root MSE          =    {res}  .9981

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}    std_efficacy{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.1320625{col 30}{space 2} .1194058{col 41}{space 1}   -1.11{col 50}{space 3}0.269{col 58}{space 4}-.3663561{col 71}{space 3} .1022312
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.0230318{col 30}{space 2} .1096133{col 41}{space 1}   -0.21{col 50}{space 3}0.834{col 58}{space 4}-.2381109{col 71}{space 3} .1920473
{txt}{space 11}Black {c |}{col 18}{res}{space 2} .0799363{col 30}{space 2} .1180537{col 41}{space 1}    0.68{col 50}{space 3}0.498{col 58}{space 4}-.1517044{col 71}{space 3}  .311577
{txt}{space 8}Hispanic {c |}{col 18}{res}{space 2}-.1140862{col 30}{space 2}  .149061{col 41}{space 1}   -0.77{col 50}{space 3}0.444{col 58}{space 4}-.4065682{col 71}{space 3} .1783958
{txt}{space 11}Women {c |}{col 18}{res}{space 2} -.326593{col 30}{space 2} .0992446{col 41}{space 1}   -3.29{col 50}{space 3}0.001{col 58}{space 4}-.5213272{col 71}{space 3}-.1318589
{txt}{space 6}BlackXrace {c |}{col 18}{res}{space 2} .2374085{col 30}{space 2} .1465475{col 41}{space 1}    1.62{col 50}{space 3}0.106{col 58}{space 4}-.0501416{col 71}{space 3} .5249587
{txt}{space 4}BlackXgender {c |}{col 18}{res}{space 2}-.1380878{col 30}{space 2} .1385173{col 41}{space 1}   -1.00{col 50}{space 3}0.319{col 58}{space 4}-.4098813{col 71}{space 3} .1337058
{txt}{space 3}HispanicXrace {c |}{col 18}{res}{space 2} -.022559{col 30}{space 2} .1655824{col 41}{space 1}   -0.14{col 50}{space 3}0.892{col 58}{space 4}-.3474587{col 71}{space 3} .3023407
{txt}{space 1}HispanicXgender {c |}{col 18}{res}{space 2} .0684687{col 30}{space 2} .1640196{col 41}{space 1}    0.42{col 50}{space 3}0.676{col 58}{space 4}-.2533646{col 71}{space 3} .3903019
{txt}{space 6}WomenXrace {c |}{col 18}{res}{space 2} .2290597{col 30}{space 2} .1286494{col 41}{space 1}    1.78{col 50}{space 3}0.075{col 58}{space 4}-.0233715{col 71}{space 3} .4814909
{txt}{space 4}WomenXgender {c |}{col 18}{res}{space 2} .1045946{col 30}{space 2} .1225884{col 41}{space 1}    0.85{col 50}{space 3}0.394{col 58}{space 4}-.1359439{col 71}{space 3} .3451331
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0023501{col 30}{space 2} .0018879{col 41}{space 1}   -1.24{col 50}{space 3}0.213{col 58}{space 4}-.0060544{col 71}{space 3} .0013543
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0369017{col 30}{space 2} .0241103{col 41}{space 1}    1.53{col 50}{space 3}0.126{col 58}{space 4}-.0104066{col 71}{space 3} .0842101
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0035741{col 30}{space 2} .0104131{col 41}{space 1}   -0.34{col 50}{space 3}0.731{col 58}{space 4}-.0240063{col 71}{space 3} .0168581
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.5435768{col 30}{space 2} .1380393{col 41}{space 1}   -3.94{col 50}{space 3}0.000{col 58}{space 4}-.8144324{col 71}{space 3}-.2727211
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .1872348{col 30}{space 2}  .169769{col 41}{space 1}    1.10{col 50}{space 3}0.270{col 58}{space 4}-.1458797{col 71}{space 3} .5203493
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} BlackXrace - HispanicXrace = 0{p_end}

{txt}       F(  1,  1080) ={res}    1.90
{txt}{col 13}Prob > F ={res}    0.1689

{p 0 7}{space 1}{text:( 1)}{space 1} BlackXgender - HispanicXgender = 0{p_end}

{txt}       F(  1,  1080) ={res}    1.25
{txt}{col 13}Prob > F ={res}    0.2645

{txt}Linear regression                               Number of obs     = {res}     1,096
                                                {txt}F(15, 1080)       =  {res}     2.72
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0339
                                                {txt}Root MSE          =    {res} .98889

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}std_elect_inte~t{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.1933103{col 30}{space 2} .1128055{col 41}{space 1}   -1.71{col 50}{space 3}0.087{col 58}{space 4}-.4146531{col 71}{space 3} .0280325
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.0196088{col 30}{space 2} .1024182{col 41}{space 1}   -0.19{col 50}{space 3}0.848{col 58}{space 4}  -.22057{col 71}{space 3} .1813524
{txt}{space 11}Black {c |}{col 18}{res}{space 2} .2501245{col 30}{space 2} .1139665{col 41}{space 1}    2.19{col 50}{space 3}0.028{col 58}{space 4} .0265037{col 71}{space 3} .4737454
{txt}{space 8}Hispanic {c |}{col 18}{res}{space 2} .0533517{col 30}{space 2} .1322233{col 41}{space 1}    0.40{col 50}{space 3}0.687{col 58}{space 4} -.206092{col 71}{space 3} .3127954
{txt}{space 11}Women {c |}{col 18}{res}{space 2} -.281714{col 30}{space 2} .0925863{col 41}{space 1}   -3.04{col 50}{space 3}0.002{col 58}{space 4}-.4633834{col 71}{space 3}-.1000447
{txt}{space 6}BlackXrace {c |}{col 18}{res}{space 2}-.2554275{col 30}{space 2} .1537582{col 41}{space 1}   -1.66{col 50}{space 3}0.097{col 58}{space 4}-.5571262{col 71}{space 3} .0462711
{txt}{space 4}BlackXgender {c |}{col 18}{res}{space 2}-.1614522{col 30}{space 2} .1403728{col 41}{space 1}   -1.15{col 50}{space 3}0.250{col 58}{space 4}-.4368864{col 71}{space 3} .1139821
{txt}{space 3}HispanicXrace {c |}{col 18}{res}{space 2} -.022407{col 30}{space 2} .1675181{col 41}{space 1}   -0.13{col 50}{space 3}0.894{col 58}{space 4}-.3511047{col 71}{space 3} .3062908
{txt}{space 1}HispanicXgender {c |}{col 18}{res}{space 2}  .146092{col 30}{space 2} .1587208{col 41}{space 1}    0.92{col 50}{space 3}0.358{col 58}{space 4} -.165344{col 71}{space 3}  .457528
{txt}{space 6}WomenXrace {c |}{col 18}{res}{space 2}  .248494{col 30}{space 2} .1312228{col 41}{space 1}    1.89{col 50}{space 3}0.059{col 58}{space 4}-.0089865{col 71}{space 3} .5059744
{txt}{space 4}WomenXgender {c |}{col 18}{res}{space 2} .0474131{col 30}{space 2} .1207673{col 41}{space 1}    0.39{col 50}{space 3}0.695{col 58}{space 4}-.1895521{col 71}{space 3} .2843784
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0021925{col 30}{space 2} .0018768{col 41}{space 1}    1.17{col 50}{space 3}0.243{col 58}{space 4}-.0014901{col 71}{space 3} .0058751
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0683121{col 30}{space 2} .0258408{col 41}{space 1}    2.64{col 50}{space 3}0.008{col 58}{space 4} .0176083{col 71}{space 3} .1190158
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0105449{col 30}{space 2} .0110446{col 41}{space 1}   -0.95{col 50}{space 3}0.340{col 58}{space 4}-.0322162{col 71}{space 3} .0111264
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.3699486{col 30}{space 2} .1550424{col 41}{space 1}   -2.39{col 50}{space 3}0.017{col 58}{space 4}-.6741671{col 71}{space 3}-.0657301
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.1466878{col 30}{space 2} .1698383{col 41}{space 1}   -0.86{col 50}{space 3}0.388{col 58}{space 4}-.4799382{col 71}{space 3} .1865627
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} BlackXrace - HispanicXrace = 0{p_end}

{txt}       F(  1,  1080) ={res}    1.36
{txt}{col 13}Prob > F ={res}    0.2440

{p 0 7}{space 1}{text:( 1)}{space 1} BlackXgender - HispanicXgender = 0{p_end}

{txt}       F(  1,  1080) ={res}    2.81
{txt}{col 13}Prob > F ={res}    0.0937

{txt}Linear regression                               Number of obs     = {res}     1,096
                                                {txt}F(15, 1080)       =  {res}    10.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1114
                                                {txt}Root MSE          =    {res} .96049

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}std_intent_sup~t{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}comm_racematch {c |}{col 18}{res}{space 2}-.0527968{col 30}{space 2} .1088615{col 41}{space 1}   -0.48{col 50}{space 3}0.628{col 58}{space 4}-.2664007{col 71}{space 3} .1608072
{txt}comm_gendermatch {c |}{col 18}{res}{space 2}-.0992455{col 30}{space 2} .1049854{col 41}{space 1}   -0.95{col 50}{space 3}0.345{col 58}{space 4}-.3052439{col 71}{space 3} .1067529
{txt}{space 11}Black {c |}{col 18}{res}{space 2} .1008467{col 30}{space 2} .1124022{col 41}{space 1}    0.90{col 50}{space 3}0.370{col 58}{space 4}-.1197048{col 71}{space 3} .3213982
{txt}{space 8}Hispanic {c |}{col 18}{res}{space 2} .0445218{col 30}{space 2} .1452838{col 41}{space 1}    0.31{col 50}{space 3}0.759{col 58}{space 4}-.2405487{col 71}{space 3} .3295923
{txt}{space 11}Women {c |}{col 18}{res}{space 2} -.381105{col 30}{space 2} .0946865{col 41}{space 1}   -4.02{col 50}{space 3}0.000{col 58}{space 4}-.5668954{col 71}{space 3}-.1953147
{txt}{space 6}BlackXrace {c |}{col 18}{res}{space 2}  .097265{col 30}{space 2} .1409138{col 41}{space 1}    0.69{col 50}{space 3}0.490{col 58}{space 4}-.1792308{col 71}{space 3} .3737608
{txt}{space 4}BlackXgender {c |}{col 18}{res}{space 2}-.1116622{col 30}{space 2} .1337031{col 41}{space 1}   -0.84{col 50}{space 3}0.404{col 58}{space 4}-.3740095{col 71}{space 3} .1506852
{txt}{space 3}HispanicXrace {c |}{col 18}{res}{space 2}-.0670021{col 30}{space 2} .1667785{col 41}{space 1}   -0.40{col 50}{space 3}0.688{col 58}{space 4}-.3942488{col 71}{space 3} .2602445
{txt}{space 1}HispanicXgender {c |}{col 18}{res}{space 2} .0201664{col 30}{space 2} .1646813{col 41}{space 1}    0.12{col 50}{space 3}0.903{col 58}{space 4}-.3029651{col 71}{space 3}  .343298
{txt}{space 6}WomenXrace {c |}{col 18}{res}{space 2} .1490351{col 30}{space 2} .1219671{col 41}{space 1}    1.22{col 50}{space 3}0.222{col 58}{space 4}-.0902842{col 71}{space 3} .3883545
{txt}{space 4}WomenXgender {c |}{col 18}{res}{space 2} .2131222{col 30}{space 2} .1175624{col 41}{space 1}    1.81{col 50}{space 3}0.070{col 58}{space 4}-.0175544{col 71}{space 3} .4437988
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0164119{col 30}{space 2} .0017837{col 41}{space 1}   -9.20{col 50}{space 3}0.000{col 58}{space 4}-.0199119{col 71}{space 3}-.0129119
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0292942{col 30}{space 2} .0239226{col 41}{space 1}    1.22{col 50}{space 3}0.221{col 58}{space 4}-.0176459{col 71}{space 3} .0762343
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0079962{col 30}{space 2} .0104348{col 41}{space 1}   -0.77{col 50}{space 3}0.444{col 58}{space 4}-.0284709{col 71}{space 3} .0124785
{txt}{space 6}income_mis {c |}{col 18}{res}{space 2}-.1687554{col 30}{space 2} .1260927{col 41}{space 1}   -1.34{col 50}{space 3}0.181{col 58}{space 4}-.4161697{col 71}{space 3} .0786589
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .9070572{col 30}{space 2} .1615866{col 41}{space 1}    5.61{col 50}{space 3}0.000{col 58}{space 4} .5899979{col 71}{space 3} 1.224116
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} BlackXrace - HispanicXrace = 0{p_end}

{txt}       F(  1,  1080) ={res}    0.69
{txt}{col 13}Prob > F ={res}    0.4050

{p 0 7}{space 1}{text:( 1)}{space 1} BlackXgender - HispanicXgender = 0{p_end}

{txt}       F(  1,  1080) ={res}    0.48
{txt}{col 13}Prob > F ={res}    0.4881
{txt}
{com}. 
. esttab X_est_std_rate_commish X_est_std_trust X_est_std_efficacy X_est_std_elect_interest X_est_std_intent_support using interactions.tex, replace nogaps compress booktabs wrap  varwidth(30) b(%9.3f) se(%9.3f) star(* 0.05 ** .01) nonotes label title("Estimated Effects of Descriptive Rep. Signals: Respondent Characteristic Interactions"\label{c -(}interactions{c )-} \scriptsize) interaction(" x ") nobase addnote("Cell entries are unstandardized OLS coefficients; robust standard errors in parentheses; $* p < .05; ** p < .01$." "Outcome variables: mean = 0, standard deviation = 1. Restricted to respondents who identified as Men or Women and White, Black, or Hispanic.")
{res}{txt}{p 0 4 2}
(file {bf}
interactions.tex{rm}
not found)
{p_end}
(output written to {browse  `"interactions.tex"'})

{com}. 
. 
. 
. **WITHOUT KNOWN (TABLE SM.A9)
. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch  Black Hispanic Women  BlackXrace BlackXgender HispanicXrace HispanicXgender WomenXrace WomenXgender age educ income income_mis if (race_black|race_hisp|race_white) & gender!=3 & commis_name!="Brandon Johnson" & commis_name!="John Daley", r
{txt}  3{com}.         
. estimates store est_`i'_XNK
{txt}  4{com}. {c )-}
{txt}
{com}. esttab est_std_rate_commish_XNK est_std_trust_XNK est_std_efficacy_XNK est_std_elect_interest_XNK est_std_intent_support_XNK using interactions_notknown.tex, replace nogaps compress booktabs wrap varwidth(30) b(%9.3f) se(%9.3f) star(* 0.05 ** .01) nonotes label title("Estimated Effects of Descriptive Rep. Signals: Respondent Characteristic Interactions (Exclude Most Recognized Commissioners)"\label{c -(}interactions_notknown{c )-} \scriptsize) addnote("Cell entries are unstandardized OLS coefficients; robust standard errors in parentheses; $* p < .05; ** p < .01$." "Outcome variables: mean = 0, standard deviation = 1.  Restricted to respondents who identified as Men or Women and White, Black, or Hispanic. Models excludes cases where respondents were treated with Commissioner Brandon Johnson or John Daley.")
{res}{txt}{p 0 4 2}
(file {bf}
interactions_notknown.tex{rm}
not found)
{p_end}
(output written to {browse  `"interactions_notknown.tex"'})

{com}. 
. 
. sum TimeAtENDOFP, det

                   {txt}Time At END OF Politics
{hline 61}
      Percentiles      Smallest
 1%    {res}       68             47
{txt} 5%    {res}       96             51
{txt}10%    {res}      119             55       {txt}Obs         {res}      1,199
{txt}25%    {res}      154             56       {txt}Sum of wgt. {res}      1,199

{txt}50%    {res}      208                      {txt}Mean          {res} 301.0684
                        {txt}Largest       Std. dev.     {res} 968.5026
{txt}75%    {res}      283           3792
{txt}90%    {res}      399           6885       {txt}Variance      {res} 937997.2
{txt}95%    {res}      569           7437       {txt}Skewness      {res} 27.23586
{txt}99%    {res}     1789          30962       {txt}Kurtosis      {res} 843.8145
{txt}
{com}. sum TotalTime, det

                         {txt}Total Time
{hline 61}
      Percentiles      Smallest
 1%    {res}      162            117
{txt} 5%    {res}      230            118
{txt}10%    {res}      282            123       {txt}Obs         {res}      1,199
{txt}25%    {res}      368            127       {txt}Sum of wgt. {res}      1,199

{txt}50%    {res}      503                      {txt}Mean          {res} 698.9316
                        {txt}Largest       Std. dev.     {res} 1643.049
{txt}75%    {res}      680          10316
{txt}90%    {res}      962          15143       {txt}Variance      {res}  2699609
{txt}95%    {res}     1290          31262       {txt}Skewness      {res} 18.17469
{txt}99%    {res}     3811          40355       {txt}Kurtosis      {res} 390.8555
{txt}
{com}. gen time_on_descriptive=(TimeAtENDOFDe-TimeAtENDOFP)
{txt}
{com}. 
. ****BY TIME ON SURVEY (FIGURE SM.A4)
. xtile timespent=TimeAtENDOFP, nq(20)
{txt}
{com}. 
. qui sum if timespent>1
{txt}
{com}. local n = r(N)
{txt}
{com}. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis i.gender race_white race_hisp race_black race_aind race_hpi race_asian race_other  i.names_index if timespent>1, r       
{txt}  3{com}. test comm_racematch 
{txt}  4{com}. test comm_gendermatch 
{txt}  5{com}. estimates store est_`i'_Q1
{txt}  6{com}. {c )-}

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,  1110) ={res}    1.23
{txt}{col 13}Prob > F ={res}    0.2673

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.84
{txt}{col 13}Prob > F ={res}    0.3589

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.02
{txt}{col 13}Prob > F ={res}    0.8756

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.81
{txt}{col 13}Prob > F ={res}    0.3672

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1110) ={res}    2.17
{txt}{col 13}Prob > F ={res}    0.1410

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.05
{txt}{col 13}Prob > F ={res}    0.8162

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1110) ={res}    2.84
{txt}{col 13}Prob > F ={res}    0.0921

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.00
{txt}{col 13}Prob > F ={res}    0.9621

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1110) ={res}    1.38
{txt}{col 13}Prob > F ={res}    0.2408

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1110) ={res}    0.12
{txt}{col 13}Prob > F ={res}    0.7277
{txt}
{com}. coefplot est_std_rate_commish_Q1 est_std_trust_Q1 est_std_efficacy_Q1 est_std_elect_interest_Q1 est_std_intent_support_Q1 , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.25).5, labsize(small)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Treatment Effect", size(medlarge)) title("> Ventile 1 (N = `n')")  text(.66 -.15 "Rating" .825 -.20 "Trust" .99 -.15 "Efficacy" 1.16 -.36 "Interest" 1.33 -.18 "Support", size(small))
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph save fx_Q1, replace
{txt}{p 0 4 2}
(file {bf}
fx_Q1.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q1.gph} saved

{com}. 
. forvalues t =2/6{c -(}
{txt}  2{com}. qui sum if timespent>`t'
{txt}  3{com}. local n = r(N)
{txt}  4{com}. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  5{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis i.gender race_white race_hisp race_black race_aind race_hpi race_asian race_other  i.names_index if timespent>`t', r     
{txt}  6{com}. disp "`i'"
{txt}  7{com}. test comm_racematch 
{txt}  8{com}. test comm_gendermatch 
{txt}  9{com}. estimates store est_`i'_Q`t'
{txt} 10{com}. {c )-}
{txt} 11{com}. coefplot est_std_rate_commish_Q`t' est_std_trust_Q`t' est_std_efficacy_Q`t' est_std_elect_interest_Q`t' est_std_intent_support_Q`t' , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.25).5, labsize(small)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Treatment Effect", size(medlarge)) title("> Ventile `t' (N = `n')")
{txt} 12{com}. graph save fx_Q`t', replace
{txt} 13{com}. {c )-}
std_rate_commish

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,  1049) ={res}    2.33
{txt}{col 13}Prob > F ={res}    0.1274

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.70
{txt}{col 13}Prob > F ={res}    0.4028
std_trust

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.27
{txt}{col 13}Prob > F ={res}    0.6033

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.51
{txt}{col 13}Prob > F ={res}    0.4740
std_efficacy

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1049) ={res}    3.59
{txt}{col 13}Prob > F ={res}    0.0584

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.00
{txt}{col 13}Prob > F ={res}    0.9704
std_elect_interest

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1049) ={res}    2.24
{txt}{col 13}Prob > F ={res}    0.1348

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.01
{txt}{col 13}Prob > F ={res}    0.9268
std_intent_support

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,  1049) ={res}    4.57
{txt}{col 13}Prob > F ={res}    0.0328

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,  1049) ={res}    0.03
{txt}{col 13}Prob > F ={res}    0.8538
{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
fx_Q2.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q2.gph} saved
std_rate_commish

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,   992) ={res}    3.55
{txt}{col 13}Prob > F ={res}    0.0597

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.54
{txt}{col 13}Prob > F ={res}    0.4626
std_trust

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.99
{txt}{col 13}Prob > F ={res}    0.3194

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.57
{txt}{col 13}Prob > F ={res}    0.4503
std_efficacy

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   992) ={res}    3.54
{txt}{col 13}Prob > F ={res}    0.0601

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.04
{txt}{col 13}Prob > F ={res}    0.8367
std_elect_interest

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   992) ={res}    2.05
{txt}{col 13}Prob > F ={res}    0.1521

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.01
{txt}{col 13}Prob > F ={res}    0.9312
std_intent_support

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   992) ={res}    4.80
{txt}{col 13}Prob > F ={res}    0.0287

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   992) ={res}    0.08
{txt}{col 13}Prob > F ={res}    0.7759
{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
fx_Q3.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q3.gph} saved
std_rate_commish

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,   930) ={res}    4.20
{txt}{col 13}Prob > F ={res}    0.0408

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.27
{txt}{col 13}Prob > F ={res}    0.6065
std_trust

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.90
{txt}{col 13}Prob > F ={res}    0.3434

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.29
{txt}{col 13}Prob > F ={res}    0.5905
std_efficacy

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   930) ={res}    3.21
{txt}{col 13}Prob > F ={res}    0.0737

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.08
{txt}{col 13}Prob > F ={res}    0.7774
std_elect_interest

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   930) ={res}    1.42
{txt}{col 13}Prob > F ={res}    0.2333

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.10
{txt}{col 13}Prob > F ={res}    0.7580
std_intent_support

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   930) ={res}    5.47
{txt}{col 13}Prob > F ={res}    0.0195

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   930) ={res}    0.20
{txt}{col 13}Prob > F ={res}    0.6541
{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
fx_Q4.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q4.gph} saved
std_rate_commish

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,   870) ={res}    3.96
{txt}{col 13}Prob > F ={res}    0.0470

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   870) ={res}    0.11
{txt}{col 13}Prob > F ={res}    0.7381
std_trust

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   870) ={res}    1.53
{txt}{col 13}Prob > F ={res}    0.2165

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   870) ={res}    0.24
{txt}{col 13}Prob > F ={res}    0.6241
std_efficacy

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   870) ={res}    2.71
{txt}{col 13}Prob > F ={res}    0.1001

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   870) ={res}    0.22
{txt}{col 13}Prob > F ={res}    0.6405
std_elect_interest

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   870) ={res}    1.61
{txt}{col 13}Prob > F ={res}    0.2053

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   870) ={res}    0.01
{txt}{col 13}Prob > F ={res}    0.9044
std_intent_support

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   870) ={res}    5.02
{txt}{col 13}Prob > F ={res}    0.0253

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   870) ={res}    0.17
{txt}{col 13}Prob > F ={res}    0.6820
{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
fx_Q5.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q5.gph} saved
std_rate_commish

{p 0 7}{space 1}{text:( 1)}{space 1} {res}comm_racematch = 0{p_end}

{txt}       F(  1,   809) ={res}    3.72
{txt}{col 13}Prob > F ={res}    0.0541

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   809) ={res}    0.00
{txt}{col 13}Prob > F ={res}    0.9687
std_trust

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   809) ={res}    2.55
{txt}{col 13}Prob > F ={res}    0.1104

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   809) ={res}    0.18
{txt}{col 13}Prob > F ={res}    0.6734
std_efficacy

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   809) ={res}    2.36
{txt}{col 13}Prob > F ={res}    0.1252

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   809) ={res}    0.03
{txt}{col 13}Prob > F ={res}    0.8610
std_elect_interest

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   809) ={res}    1.39
{txt}{col 13}Prob > F ={res}    0.2391

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   809) ={res}    0.12
{txt}{col 13}Prob > F ={res}    0.7338
std_intent_support

{p 0 7}{space 1}{text:( 1)}{space 1} comm_racematch = 0{p_end}

{txt}       F(  1,   809) ={res}    4.47
{txt}{col 13}Prob > F ={res}    0.0347

{p 0 7}{space 1}{text:( 1)}{space 1} comm_gendermatch = 0{p_end}

{txt}       F(  1,   809) ={res}    0.26
{txt}{col 13}Prob > F ={res}    0.6084
{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}{p 0 4 2}
(file {bf}
fx_Q6.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:fx_Q6.gph} saved

{com}. 
. graph combine fx_Q1.gph fx_Q2.gph fx_Q3.gph fx_Q4.gph fx_Q5.gph fx_Q6.gph,xcommon scheme(s1mono)
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph export "fx_by_time.eps", as(eps) replace
{txt}{p 0 4 2}
(file {bf}
fx_by_time.eps{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
fx_by_time.eps{rm}
saved as
EPS
format
{p_end}

{com}. graph export "fx_by_time.tif", as(tif) replace
{txt}{p 0 4 2}
(file {bf}
fx_by_time.tif{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
fx_by_time.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. 
. ***MAIN, ROBUST TO NO NAME CONTROLS (FIGURE SM.A3)
. foreach i in std_rate_commish std_trust std_efficacy std_elect_interest std_intent_support{c -(}
{txt}  2{com}. qui reg `i' comm_racematch comm_gendermatch age educ income income_mis Women Other_Gend race_white race_hisp race_black race_aind race_hpi race_asian race_other, r     
{txt}  3{com}. estimates store nn_est_`i'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. coefplot est_std_rate_commish est_std_trust est_std_efficacy est_std_elect_interest est_std_intent_support , keep(comm_racematch comm_gendermatch) xline(0, lcolor(black) lwidth(thin)) level(95) graphregion(fcolor(white) lcolor(none) ilcolor(none) color(white) lwidth(large)) xlabel(-.5(.125).5, labsize(small)) xscale(lw(none)) ylabel(,labsize(medium) angle(vertical)) mcolor(black) msize(small) label legend(off) xtitle("Estimated Treatment Effect (in Standard Deviations)", size(medlarge))  text(.66 .32 "Board Job Rating" .825 .14 "Trust Commissioner" .99 .30 "Efficacy" 1.16 .32 "Interest in Election" 1.33 .34 "Intent to Support" 1.66 -.23 "Board Job Rating" 1.825 -.22 "Trust Commissioner" 1.99 -.21 "Efficacy" 2.16 -.26 "Interest in Election" 2.33 -.35 "Intent to Support",  size(small))
{res}{p 0 4 2}
{txt}(note:  named style
large not found in class
linewidth,  default attributes used)
{p_end}
{txt}{p 0 8} (note:  linewidth  {txt}not found in scheme, default attributes used){p_end}
{res}{txt}
{com}. graph save full_sample_noname_ctrls, replace
{txt}{p 0 4 2}
(file {bf}
full_sample_noname_ctrls.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:full_sample_noname_ctrls.gph} saved

{com}. graph export "direct_fx_noname_ctrls.eps", as(eps) replace
{txt}{p 0 4 2}
(file {bf}
direct_fx_noname_ctrls.eps{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
direct_fx_noname_ctrls.eps{rm}
saved as
EPS
format
{p_end}

{com}. 
. 
. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\onedr\Dropbox\Cook County Community Survey (Dana and Dave)\Student Projects\Descriptive Representation\RandP Submission\log_of_analysis.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}27 Jun 2024, 13:12:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}